Digital modeling of disease on crops on agronomics fields

ABSTRACT

A system and method for identifying a probability of disease affecting a crop based on data received over a network is described herein, and may be implemented using computers for providing improvements in plant pathology, plant pest control, agriculture, or agricultural management. In an embodiment, a server computer receives environmental risk data, crop data, and crop management data relating to one or more crops on a field. Agricultural intelligence computer system  130  computes one or more crop risk factors based, at least in part, the crop data, one or more environmental risk factors based, at least in part, the environmental data, and one or more crop management risk factors based, at least in part, on the crop management data. Using a digital model of disease probability, agricultural intelligence computer system  130  computes a probability of onset of a particular disease for the one or more crops on the field based, at least in part, on the one or more crop risk factors, the one or more environmental risk factors, and the one or more crop management factors.

CROSS-REFERENCE TO RELATED APPLICATIONS, BENEFIT CLAIM

This application claims the benefit as a Continuation of applicationSer. No. 15/820,322 filed Nov. 21, 2017, the entire contents of which ishereby incorporated by as if fully set forth herein, under U.S.C. § 120.The applicant hereby rescinds any disclaimer of claim scope in theparent application or the prosecution history thereof and advise theUSPTO that the claims in this application may be broader than any claimin the parent application.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyright orrights whatsoever. © 2015-2017 The Climate Corporation.

FIELD OF THE DISCLOSURE

The present disclosure relates to digital modeling of agronomic fieldsusing a server computer, using programmed process to provideimprovements in the technologies of plant pathology, plant pest control,agriculture, or agricultural management. Specifically, the presentdisclosure relates to modeling a likelihood of particular diseasespresenting on a field based on field data and then using the resultingdata models to improve plant pathology, plant pest control, agriculture,or agricultural management.

BACKGROUND

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

Field managers are faced with a wide variety of decisions to make withrespect to the management of agricultural fields. These decisions rangefrom determining what crop to plant, which type of seed to plant for thecrop, when to harvest a crop, whether to perform tillage, irrigation,application of pesticides, application of fungicides, and application offertilizer, and what types of pesticides, fungicides, and fertilizers toapply.

Field managers must also contend with outside phenomena which affect theyield of their crops. For instance, certain diseases can have a largeimpact on the health of a crop and thus the amount the crop yields. Cornin particular is susceptible to diseases such as northern leaf blightand gray leaf blot.

In order to combat the effects of diseases on crops, a field manager mayapply fungicide to a field. The fungicide reduces the risk of onset ofdiseases and, in some cases, can reduce the effects of diseasescurrently on the field. While applying fungicide is useful in preventingdisease, it also comes at a cost. Applying fungicide to a field that isnot in danger of being affected by disease can be a waste that costs afield manager some of the total revenue from sale of the crop.

Generally, a field manager has no good way of determining whether thefield is currently being affected by disease or is about to be affectedby a disease. A field manager maintaining hundreds of acres of crops maynot have the capability to manually check each crop for signs ofdisease. Additionally, a field manager is unable to determine when, ifever, a disease may present itself on the crops.

Thus, there is a need for a system or method which tracks the likelihoodof onset of a disease on a crop.

SUMMARY

The appended claims may serve as a summary of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using agronomic data provided by one or more datasources.

FIG. 4 is a block diagram that illustrates a computer system upon whichan embodiment of the invention may be implemented.

FIG. 5 depicts an example embodiment of a timeline view for data entry.

FIG. 6 depicts an example embodiment of a spreadsheet view for dataentry.

FIG. 7 depicts a method for determining a risk of disease of a crop on afield based on received data regarding the crop.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be apparent, however,that embodiments may be practiced without these specific details. Inother instances, well-known structures and devices are shown in blockdiagram form in order to avoid unnecessarily obscuring the presentdisclosure. Embodiments are disclosed in sections according to thefollowing outline:

-   -   1. GENERAL OVERVIEW    -   2. EXAMPLE AGRICULTURAL INTELLIGENCE COMPUTER SYSTEM        -   2.1. STRUCTURAL OVERVIEW        -   2.2. APPLICATION PROGRAM OVERVIEW        -   2.3. DATA INGEST TO THE COMPUTER SYSTEM        -   2.4. PROCESS OVERVIEW—AGRONOMIC MODEL TRAINING        -   2.5. IMPLEMENTATION EXAMPLE—HARDWARE OVERVIEW    -   3. DETERMINING RISK OF DISEASE        -   3.1. RECEIVED DATA        -   3.2. FACTOR GENERATION        -   3.3. DIGITAL DISEASE MODELING        -   3.4. DATA USAGE    -   4. BENEFITS OF CERTAIN EMBODIMENTS    -   5. EXTENSIONS AND ALTERNATIVES

1. GENERAL OVERVIEW

Systems and methods for tracking disease onset in one or more fields aredescribed herein. In an embodiment, weather data is used to determine anenvironmental risk of disease presenting on the crop. Using theenvironmental risk, data relating to the crop such as the hybridsusceptibility and/or relative maturity, and data relating to themanagement of the field, such as tillage, harvesting, and/or productapplication, the server computer models a risk of the disease presentingon the crop over a particular timeframe. If the server computerdetermines that the disease has or will present on the crop, the servercomputer is able to make recommendations for preventing the diseaseand/or generate a script which is used to control an implement on thefield, thereby causing the implement to spray the field with a fungicideor take other disease preventative measures.

In an embodiment, a method comprises receiving environmental risk data,crop data, and crop management data relating to one or more crops on afield; computing one or more crop risk factors based, at least in part,on the crop data; computing one or more environmental risk factorsbased, at least in part, on the environmental risk data; computing oneor more crop management risk factors based, at least in part, on thecrop management data; using a digital model of disease probability,computing a probability of onset of a particular disease for the one ormore crops on the field based, at least in part, on the one or more croprisk factors, the one or more environmental risk factors, and the one ormore crop management factors.

2. EXAMPLE AGRICULTURAL INTELLIGENCE COMPUTER System

2.1 Structural Overview

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate. In oneembodiment, a user 102 owns, operates or possesses a field managercomputing device 104 in a field location or associated with a fieldlocation such as a field intended for agricultural activities or amanagement location for one or more agricultural fields. The fieldmanager computer device 104 is programmed or configured to provide fielddata 106 to an agricultural intelligence computer system 130 via one ormore networks 109.

Examples of field data 106 include (a) identification data (for example,acreage, field name, field identifiers, geographic identifiers, boundaryidentifiers, crop identifiers, and any other suitable data that may beused to identify farm land, such as a common land unit (CLU), lot andblock number, a parcel number, geographic coordinates and boundaries,Farm Serial Number (FSN), farm number, tract number, field number,section, township, and/or range), (b) harvest data (for example, croptype, crop variety, crop rotation, whether the crop is grownorganically, harvest date, Actual Production History (APH), expectedyield, yield, crop price, crop revenue, grain moisture, tillagepractice, and previous growing season information), (c) soil data (forexample, type, composition, pH, organic matter (OM), cation exchangecapacity (CEC)), (d) planting data (for example, planting date, seed(s)type, relative maturity (RM) of planted seed(s), seed population), (e)fertilizer data (for example, nutrient type (Nitrogen, Phosphorous,Potassium), application type, application date, amount, source, method),(f) pesticide data (for example, pesticide, herbicide, fungicide, othersubstance or mixture of substances intended for use as a plantregulator, defoliant, or desiccant, application date, amount, source,method), (g) irrigation data (for example, application date, amount,source, method), (h) weather data (for example, precipitation, rainfallrate, predicted rainfall, water runoff rate region, temperature, wind,forecast, pressure, visibility, clouds, heat index, dew point, humidity,snow depth, air quality, sunrise, sunset), (i) imagery data (forexample, imagery and light spectrum information from an agriculturalapparatus sensor, camera, computer, smartphone, tablet, unmanned aerialvehicle, planes or satellite), (j) scouting observations (photos,videos, free form notes, voice recordings, voice transcriptions, weatherconditions (temperature, precipitation (current and over time), soilmoisture, crop growth stage, wind velocity, relative humidity, dewpoint, black layer)), and (k) soil, seed, crop phenology, pest anddisease reporting, and predictions sources and databases.

A data server computer 108 is communicatively coupled to agriculturalintelligence computer system 130 and is programmed or configured to sendexternal data 110 to agricultural intelligence computer system 130 viathe network(s) 109. The external data server computer 108 may be ownedor operated by the same legal person or entity as the agriculturalintelligence computer system 130, or by a different person or entitysuch as a government agency, non-governmental organization (NGO), and/ora private data service provider. Examples of external data includeweather data, imagery data, soil data, or statistical data relating tocrop yields, among others. External data 110 may consist of the sametype of information as field data 106. In some embodiments, the externaldata 110 is provided by an external data server 108 owned by the sameentity that owns and/or operates the agricultural intelligence computersystem 130. For example, the agricultural intelligence computer system130 may include a data server focused exclusively on a type of data thatmight otherwise be obtained from third party sources, such as weatherdata. In some embodiments, an external data server 108 may actually beincorporated within the system 130.

An agricultural apparatus 111 may have one or more remote sensors 112fixed thereon, which sensors are communicatively coupled either directlyor indirectly via agricultural apparatus 111 to the agriculturalintelligence computer system 130 and are programmed or configured tosend sensor data to agricultural intelligence computer system 130.Examples of agricultural apparatus 111 include tractors, combines,harvesters, planters, trucks, fertilizer equipment, unmanned aerialvehicles, and any other item of physical machinery or hardware,typically mobile machinery, and which may be used in tasks associatedwith agriculture. In some embodiments, a single unit of apparatus 111may comprise a plurality of sensors 112 that are coupled locally in anetwork on the apparatus; controller area network (CAN) is example ofsuch a network that can be installed in combines or harvesters.Application controller 114 is communicatively coupled to agriculturalintelligence computer system 130 via the network(s) 109 and isprogrammed or configured to receive one or more scripts to control anoperating parameter of an agricultural vehicle or implement from theagricultural intelligence computer system 130. For instance, acontroller area network (CAN) bus interface may be used to enablecommunications from the agricultural intelligence computer system 130 tothe agricultural apparatus 111, such as how the CLIMATE FIELDVIEW DRIVE,available from The Climate Corporation, San Francisco, Calif., is used.Sensor data may consist of the same type of information as field data106. In some embodiments, remote sensors 112 may not be fixed to anagricultural apparatus 111 but may be remotely located in the field andmay communicate with network 109.

The apparatus 111 may comprise a cab computer 115 that is programmedwith a cab application, which may comprise a version or variant of themobile application for device 104 that is further described in othersections herein. In an embodiment, cab computer 115 comprises a compactcomputer, often a tablet-sized computer or smartphone, with a graphicalscreen display, such as a color display, that is mounted within anoperator's cab of the apparatus 111. Cab computer 115 may implement someor all of the operations and functions that are described further hereinfor the mobile computer device 104.

The network(s) 109 broadly represent any combination of one or more datacommunication networks including local area networks, wide areanetworks, internetworks or internets, using any of wireline or wirelesslinks, including terrestrial or satellite links. The network(s) may beimplemented by any medium or mechanism that provides for the exchange ofdata between the various elements of FIG. 1 . The various elements ofFIG. 1 may also have direct (wired or wireless) communications links.The sensors 112, controller 114, external data server computer 108, andother elements of the system each comprise an interface compatible withthe network(s) 109 and are programmed or configured to use standardizedprotocols for communication across the networks such as TCP/IP,Bluetooth, CAN protocol and higher-layer protocols such as HTTP, TLS,and the like.

Agricultural intelligence computer system 130 is programmed orconfigured to receive field data 106 from field manager computing device104, external data 110 from external data server computer 108, andsensor data from remote sensor 112. Agricultural intelligence computersystem 130 may be further configured to host, use or execute one or morecomputer programs, other software elements, digitally programmed logicsuch as FPGAs or ASICs, or any combination thereof to performtranslation and storage of data values, construction of digital modelsof one or more crops on one or more fields, generation ofrecommendations and notifications, and generation and sending of scriptsto application controller 114, in the manner described further in othersections of this disclosure.

In an embodiment, agricultural intelligence computer system 130 isprogrammed with or comprises a communication layer 132, presentationlayer 134, data management layer 140, hardware/virtualization layer 150,and model and field data repository 160. “Layer,” in this context,refers to any combination of electronic digital interface circuits,microcontrollers, firmware such as drivers, and/or computer programs orother software elements.

Communication layer 132 may be programmed or configured to performinput/output interfacing functions including sending requests to fieldmanager computing device 104, external data server computer 108, andremote sensor 112 for field data, external data, and sensor datarespectively. Communication layer 132 may be programmed or configured tosend the received data to model and field data repository 160 to bestored as field data 106.

Presentation layer 134 may be programmed or configured to generate agraphical user interface (GUI) to be displayed on field managercomputing device 104, cab computer 115 or other computers that arecoupled to the system 130 through the network 109. The GUI may comprisecontrols for inputting data to be sent to agricultural intelligencecomputer system 130, generating requests for models and/orrecommendations, and/or displaying recommendations, notifications,models, and other field data.

Data management layer 140 may be programmed or configured to manage readoperations and write operations involving the repository 160 and otherfunctional elements of the system, including queries and result setscommunicated between the functional elements of the system and therepository. Examples of data management layer 140 include JDBC, SQLserver interface code, and/or HADOOP interface code, among others.Repository 160 may comprise a database. As used herein, the term“database” may refer to either a body of data, a relational databasemanagement system (RDBMS), or to both. As used herein, a database maycomprise any collection of data including hierarchical databases,relational databases, flat file databases, object-relational databases,object oriented databases, and any other structured collection ofrecords or data that is stored in a computer system. Examples of RDBMS'sinclude, but are not limited to including, ORACLE®, MYSQL, IBM® DB2,MICROSOFT® SQL SERVER, SYBASE®, and POSTGRESQL databases. However, anydatabase may be used that enables the systems and methods describedherein.

When field data 106 is not provided directly to the agriculturalintelligence computer system via one or more agricultural machines oragricultural machine devices that interacts with the agriculturalintelligence computer system, the user may be prompted via one or moreuser interfaces on the user device (served by the agriculturalintelligence computer system) to input such information. In an exampleembodiment, the user may specify identification data by accessing a mapon the user device (served by the agricultural intelligence computersystem) and selecting specific CLUs that have been graphically shown onthe map. In an alternative embodiment, the user 102 may specifyidentification data by accessing a map on the user device (served by theagricultural intelligence computer system 130) and drawing boundaries ofthe field over the map. Such CLU selection or map drawings representgeographic identifiers. In alternative embodiments, the user may specifyidentification data by accessing field identification data (provided asshape files or in a similar format) from the U. S. Department ofAgriculture Farm Service Agency or other source via the user device andproviding such field identification data to the agriculturalintelligence computer system.

In an example embodiment, the agricultural intelligence computer system130 is programmed to generate and cause displaying a graphical userinterface comprising a data manager for data input. After one or morefields have been identified using the methods described above, the datamanager may provide one or more graphical user interface widgets whichwhen selected can identify changes to the field, soil, crops, tillage,or nutrient practices. The data manager may include a timeline view, aspreadsheet view, and/or one or more editable programs.

FIG. 5 depicts an example embodiment of a timeline view for data entry.Using the display depicted in FIG. 5 , a user computer can input aselection of a particular field and a particular date for the additionof event. Events depicted at the top of the timeline may includeNitrogen, Planting, Practices, and Soil. To add a nitrogen applicationevent, a user computer may provide input to select the nitrogen tab. Theuser computer may then select a location on the timeline for aparticular field in order to indicate an application of nitrogen on theselected field. In response to receiving a selection of a location onthe timeline for a particular field, the data manager may display a dataentry overlay, allowing the user computer to input data pertaining tonitrogen applications, planting procedures, soil application, tillageprocedures, irrigation practices, or other information relating to theparticular field. For example, if a user computer selects a portion ofthe timeline and indicates an application of nitrogen, then the dataentry overlay may include fields for inputting an amount of nitrogenapplied, a date of application, a type of fertilizer used, and any otherinformation related to the application of nitrogen.

In an embodiment, the data manager provides an interface for creatingone or more programs. “Program,” in this context, refers to a set ofdata pertaining to nitrogen applications, planting procedures, soilapplication, tillage procedures, irrigation practices, or otherinformation that may be related to one or more fields, and that can bestored in digital data storage for reuse as a set in other operations.After a program has been created, it may be conceptually applied to oneor more fields and references to the program may be stored in digitalstorage in association with data identifying the fields. Thus, insteadof manually entering identical data relating to the same nitrogenapplications for multiple different fields, a user computer may create aprogram that indicates a particular application of nitrogen and thenapply the program to multiple different fields. For example, in thetimeline view of FIG. 5 , the top two timelines have the “Fall applied”program selected, which includes an application of 150 lbs N/ac in earlyApril. The data manager may provide an interface for editing a program.In an embodiment, when a particular program is edited, each field thathas selected the particular program is edited. For example, in FIG. 5 ,if the “Fall applied” program is edited to reduce the application ofnitrogen to 130 lbs N/ac, the top two fields may be updated with areduced application of nitrogen based on the edited program.

In an embodiment, in response to receiving edits to a field that has aprogram selected, the data manager removes the correspondence of thefield to the selected program. For example, if a nitrogen application isadded to the top field in FIG. 5 , the interface may update to indicatethat the “Fall applied” program is no longer being applied to the topfield. While the nitrogen application in early April may remain, updatesto the “Fall applied” program would not alter the April application ofnitrogen.

FIG. 6 depicts an example embodiment of a spreadsheet view for dataentry. Using the display depicted in FIG. 6 , a user can create and editinformation for one or more fields. The data manager may includespreadsheets for inputting information with respect to Nitrogen,Planting, Practices, and Soil as depicted in FIG. 6 . To edit aparticular entry, a user computer may select the particular entry in thespreadsheet and update the values. For example, FIG. 6 depicts anin-progress update to a target yield value for the second field.Additionally, a user computer may select one or more fields in order toapply one or more programs. In response to receiving a selection of aprogram for a particular field, the data manager may automaticallycomplete the entries for the particular field based on the selectedprogram. As with the timeline view, the data manager may update theentries for each field associated with a particular program in responseto receiving an update to the program. Additionally, the data managermay remove the correspondence of the selected program to the field inresponse to receiving an edit to one of the entries for the field.

In an embodiment, model and field data is stored in model and field datarepository 160. Model data comprises data models created for one or morefields. For example, a crop model may include a digitally constructedmodel of the development of a crop on the one or more fields. “Model,”in this context, refers to an electronic digitally stored set ofexecutable instructions and data values, associated with one another,which are capable of receiving and responding to a programmatic or otherdigital call, invocation, or request for resolution based upon specifiedinput values, to yield one or more stored output values that can serveas the basis of computer-implemented recommendations, output datadisplays, or machine control, among other things. Persons of skill inthe field find it convenient to express models using mathematicalequations, but that form of expression does not confine the modelsdisclosed herein to abstract concepts; instead, each model herein has apractical application in a computer in the form of stored executableinstructions and data that implement the model using the computer. Themodel may include a model of past events on the one or more fields, amodel of the current status of the one or more fields, and/or a model ofpredicted events on the one or more fields. Model and field data may bestored in data structures in memory, rows in a database table, in flatfiles or spreadsheets, or other forms of stored digital data.

In an embodiment, each of factor computation instructions 136 anddisease modeling instructions 138 comprises a set of one or more pagesof main memory, such as RAM, in the agricultural intelligence computersystem 130 into which executable instructions have been loaded and whichwhen executed cause the agricultural intelligence computing system toperform the functions or operations that are described herein withreference to those modules. For example, the factor computationinstructions 136 may comprise a set of pages in RAM that containinstructions which when executed cause performing the factor computationfunctions that are described herein. The instructions may be in machineexecutable code in the instruction set of a CPU and may have beencompiled based upon source code written in JAVA, C, C++, OBJECTIVE-C, orany other human-readable programming language or environment, alone orin combination with scripts in JAVASCRIPT, other scripting languages andother programming source text. The term “pages” is intended to referbroadly to any region within main memory and the specific terminologyused in a system may vary depending on the memory architecture orprocessor architecture. In another embodiment, each of factorcomputation instructions 136 and disease modeling instructions 138 alsomay represent one or more files or projects of source code that aredigitally stored in a mass storage device such as non-volatile RAM ordisk storage, in the agricultural intelligence computer system 130 or aseparate repository system, which when compiled or interpreted causegenerating executable instructions which when executed cause theagricultural intelligence computing system to perform the functions oroperations that are described herein with reference to those modules. Inother words, the drawing figure may represent the manner in whichprogrammers or software developers organize and arrange source code forlater compilation into an executable, or interpretation into bytecode orthe equivalent, for execution by the agricultural intelligence computersystem 130.

Hardware/virtualization layer 150 comprises one or more centralprocessing units (CPUs), memory controllers, and other devices,components, or elements of a computer system such as volatile ornon-volatile memory, non-volatile storage such as disk, and I/O devicesor interfaces as illustrated and described, for example, in connectionwith FIG. 4 . The layer 150 also may comprise programmed instructionsthat are configured to support virtualization, containerization, orother technologies.

For purposes of illustrating a clear example, FIG. 1 shows a limitednumber of instances of certain functional elements. However, in otherembodiments, there may be any number of such elements. For example,embodiments may use thousands or millions of different mobile computingdevices 104 associated with different users. Further, the system 130and/or external data server computer 108 may be implemented using two ormore processors, cores, clusters, or instances of physical machines orvirtual machines, configured in a discrete location or co-located withother elements in a datacenter, shared computing facility or cloudcomputing facility.

2.2. Application Program Overview

In an embodiment, the implementation of the functions described hereinusing one or more computer programs or other software elements that areloaded into and executed using one or more general-purpose computerswill cause the general-purpose computers to be configured as aparticular machine or as a computer that is specially adapted to performthe functions described herein. Further, each of the flow diagrams thatare described further herein may serve, alone or in combination with thedescriptions of processes and functions in prose herein, as algorithms,plans or directions that may be used to program a computer or logic toimplement the functions that are described. In other words, all theprose text herein, and all the drawing figures, together are intended toprovide disclosure of algorithms, plans or directions that aresufficient to permit a skilled person to program a computer to performthe functions that are described herein, in combination with the skilland knowledge of such a person given the level of skill that isappropriate for inventions and disclosures of this type.

In an embodiment, user 102 interacts with agricultural intelligencecomputer system 130 using field manager computing device 104 configuredwith an operating system and one or more application programs or apps;the field manager computing device 104 also may interoperate with theagricultural intelligence computer system independently andautomatically under program control or logical control and direct userinteraction is not always required. Field manager computing device 104broadly represents one or more of a smart phone, PDA, tablet computingdevice, laptop computer, desktop computer, workstation, or any othercomputing device capable of transmitting and receiving information andperforming the functions described herein. Field manager computingdevice 104 may communicate via a network using a mobile applicationstored on field manager computing device 104, and in some embodiments,the device may be coupled using a cable 113 or connector to the sensor112 and/or controller 114. A particular user 102 may own, operate orpossess and use, in connection with system 130, more than one fieldmanager computing device 104 at a time.

The mobile application may provide client-side functionality, via thenetwork to one or more mobile computing devices. In an exampleembodiment, field manager computing device 104 may access the mobileapplication via a web browser or a local client application or app.Field manager computing device 104 may transmit data to, and receivedata from, one or more front-end servers, using web-based protocols orformats such as HTTP, XML, and/or JSON, or app-specific protocols. In anexample embodiment, the data may take the form of requests and userinformation input, such as field data, into the mobile computing device.In some embodiments, the mobile application interacts with locationtracking hardware and software on field manager computing device 104which determines the location of field manager computing device 104using standard tracking techniques such as multilateration of radiosignals, the global positioning system (GPS), WiFi positioning systems,or other methods of mobile positioning. In some cases, location data orother data associated with the device 104, user 102, and/or useraccount(s) may be obtained by queries to an operating system of thedevice or by requesting an app on the device to obtain data from theoperating system.

In an embodiment, field manager computing device 104 sends field data106 to agricultural intelligence computer system 130 comprising orincluding, but not limited to, data values representing one or more of:a geographical location of the one or more fields, tillage informationfor the one or more fields, crops planted in the one or more fields, andsoil data extracted from the one or more fields. Field manager computingdevice 104 may send field data 106 in response to user input from user102 specifying the data values for the one or more fields. Additionally,field manager computing device 104 may automatically send field data 106when one or more of the data values becomes available to field managercomputing device 104. For example, field manager computing device 104may be communicatively coupled to remote sensor 112 and/or applicationcontroller 114. In response to receiving data indicating thatapplication controller 114 released water onto the one or more fields,field manager computing device 104 may send field data 106 toagricultural intelligence computer system 130 indicating that water wasreleased on the one or more fields. Field data 106 identified in thisdisclosure may be input and communicated using electronic digital datathat is communicated between computing devices using parameterized URLsover HTTP, or another suitable communication or messaging protocol.

A commercial example of the mobile application is CLIMATE FIELDVIEW,commercially available from The Climate Corporation, San Francisco,Calif. The CLIMATE FIELDVIEW application, or other applications, may bemodified, extended, or adapted to include features, functions, andprogramming that have not been disclosed earlier than the filing date ofthis disclosure. In one embodiment, the mobile application comprises anintegrated software platform that allows a grower to make fact-baseddecisions for their operation because it combines historical data aboutthe grower's fields with any other data that the grower wishes tocompare. The combinations and comparisons may be performed in real timeand are based upon scientific models that provide potential scenarios topermit the grower to make better, more informed decisions.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution. In FIG. 2 , each named element represents a regionof one or more pages of RAM or other main memory, or one or more blocksof disk storage or other non-volatile storage, and the programmedinstructions within those regions. In one embodiment, in view (a), amobile computer application 200 comprises account-fields-dataingestion-sharing instructions 202, overview and alert instructions 204,digital map book instructions 206, seeds and planting instructions 208,nitrogen instructions 210, weather instructions 212, field healthinstructions 214, and performance instructions 216.

In one embodiment, a mobile computer application 200 comprisesaccount-fields-data ingestion-sharing instructions 202 which areprogrammed to receive, translate, and ingest field data from third partysystems via manual upload or APIs. Data types may include fieldboundaries, yield maps, as-planted maps, soil test results, as-appliedmaps, and/or management zones, among others. Data formats may includeshape files, native data formats of third parties, and/or farmmanagement information system (FMIS) exports, among others. Receivingdata may occur via manual upload, e-mail with attachment, external APIsthat push data to the mobile application, or instructions that call APIsof external systems to pull data into the mobile application. In oneembodiment, mobile computer application 200 comprises a data inbox. Inresponse to receiving a selection of the data inbox, the mobile computerapplication 200 may display a graphical user interface for manuallyuploading data files and importing uploaded files to a data manager.

In one embodiment, digital map book instructions 206 comprise field mapdata layers stored in device memory and are programmed with datavisualization tools and geospatial field notes. This provides growerswith convenient information close at hand for reference, logging andvisual insights into field performance. In one embodiment, overview andalert instructions 204 are programmed to provide an operation-wide viewof what is important to the grower, and timely recommendations to takeaction or focus on particular issues. This permits the grower to focustime on what needs attention, to save time and preserve yield throughoutthe season. In one embodiment, seeds and planting instructions 208 areprogrammed to provide tools for seed selection, hybrid placement, andscript creation, including variable rate (VR) script creation, basedupon scientific models and empirical data. This enables growers tomaximize yield or return on investment through optimized seed purchase,placement and population.

In one embodiment, script generation instructions 205 are programmed toprovide an interface for generating scripts, including variable rate(VR) fertility scripts. The interface enables growers to create scriptsfor field implements, such as nutrient applications, planting, andirrigation. For example, a planting script interface may comprise toolsfor identifying a type of seed for planting. Upon receiving a selectionof the seed type, mobile computer application 200 may display one ormore fields broken into management zones, such as the field map datalayers created as part of digital map book instructions 206. In oneembodiment, the management zones comprise soil zones along with a panelidentifying each soil zone and a soil name, texture, drainage for eachzone, or other field data. Mobile computer application 200 may alsodisplay tools for editing or creating such, such as graphical tools fordrawing management zones, such as soil zones, over a map of one or morefields. Planting procedures may be applied to all management zones ordifferent planting procedures may be applied to different subsets ofmanagement zones. When a script is created, mobile computer application200 may make the script available for download in a format readable byan application controller, such as an archived or compressed format.Additionally, and/or alternatively, a script may be sent directly to cabcomputer 115 from mobile computer application 200 and/or uploaded to oneor more data servers and stored for further use.

In one embodiment, nitrogen instructions 210 are programmed to providetools to inform nitrogen decisions by visualizing the availability ofnitrogen to crops. This enables growers to maximize yield or return oninvestment through optimized nitrogen application during the season.Example programmed functions include displaying images such as SSURGOimages to enable drawing of application zones and/or images generatedfrom subfield soil data, such as data obtained from sensors, at a highspatial resolution (as fine as 10 meters or smaller because of theirproximity to the soil); upload of existing grower-defined zones;providing an application graph and/or a map to enable tuningapplication(s) of nitrogen across multiple zones; output of scripts todrive machinery; tools for mass data entry and adjustment; and/or mapsfor data visualization, among others. “Mass data entry,” in thiscontext, may mean entering data once and then applying the same data tomultiple fields that have been defined in the system; example data mayinclude nitrogen application data that is the same for many fields ofthe same grower, but such mass data entry applies to the entry of anytype of field data into the mobile computer application 200. Forexample, nitrogen instructions 210 may be programmed to acceptdefinitions of nitrogen planting and practices programs and to acceptuser input specifying to apply those programs across multiple fields.“Nitrogen planting programs,” in this context, refers to a stored, namedset of data that associates: a name, color code or other identifier, oneor more dates of application, types of material or product for each ofthe dates and amounts, method of application or incorporation such asinjected or knifed in, and/or amounts or rates of application for eachof the dates, crop or hybrid that is the subject of the application,among others. “Nitrogen practices programs,” in this context, refers toa stored, named set of data that associates: a practices name; aprevious crop; a tillage system; a date of primarily tillage; one ormore previous tillage systems that were used; one or more indicators ofapplication type, such as manure, that were used. Nitrogen instructions210 also may be programmed to generate and cause displaying a nitrogengraph, which indicates projections of plant use of the specifiednitrogen and whether a surplus or shortfall is predicted; in someembodiments, different color indicators may signal a magnitude ofsurplus or magnitude of shortfall. In one embodiment, a nitrogen graphcomprises a graphical display in a computer display device comprising aplurality of rows, each row associated with and identifying a field;data specifying what crop is planted in the field, the field size, thefield location, and a graphic representation of the field perimeter; ineach row, a timeline by month with graphic indicators specifying eachnitrogen application and amount at points correlated to month names; andnumeric and/or colored indicators of surplus or shortfall, in whichcolor indicates magnitude.

In one embodiment, the nitrogen graph may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen graph. The user may then use his optimized nitrogen graph andthe related nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. Nitrogeninstructions 210 also may be programmed to generate and cause displayinga nitrogen map, which indicates projections of plant use of thespecified nitrogen and whether a surplus or shortfall is predicted; insome embodiments, different color indicators may signal a magnitude ofsurplus or magnitude of shortfall. The nitrogen map may displayprojections of plant use of the specified nitrogen and whether a surplusor shortfall is predicted for different times in the past and the future(such as daily, weekly, monthly or yearly) using numeric and/or coloredindicators of surplus or shortfall, in which color indicates magnitude.In one embodiment, the nitrogen map may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen map, such as to obtain a preferred amount of surplus toshortfall. The user may then use his optimized nitrogen map and therelated nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. In otherembodiments, similar instructions to the nitrogen instructions 210 couldbe used for application of other nutrients (such as phosphorus andpotassium) application of pesticide, and irrigation programs.

In one embodiment, weather instructions 212 are programmed to providefield-specific recent weather data and forecasted weather information.This enables growers to save time and have an efficient integrateddisplay with respect to daily operational decisions.

In one embodiment, field health instructions 214 are programmed toprovide timely remote sensing images highlighting in-season cropvariation and potential concerns. Example programmed functions includecloud checking, to identify possible clouds or cloud shadows;determining nitrogen indices based on field images; graphicalvisualization of scouting layers, including, for example, those relatedto field health, and viewing and/or sharing of scouting notes; and/ordownloading satellite images from multiple sources and prioritizing theimages for the grower, among others.

In one embodiment, performance instructions 216 are programmed toprovide reports, analysis, and insight tools using on-farm data forevaluation, insights and decisions. This enables the grower to seekimproved outcomes for the next year through fact-based conclusions aboutwhy return on investment was at prior levels, and insight intoyield-limiting factors. The performance instructions 216 may beprogrammed to communicate via the network(s) 109 to back-end analyticsprograms executed at agricultural intelligence computer system 130and/or external data server computer 108 and configured to analyzemetrics such as yield, hybrid, population, SSURGO, soil tests, orelevation, among others. Programmed reports and analysis may includeyield variability analysis, benchmarking of yield and other metricsagainst other growers based on anonymized data collected from manygrowers, or data for seeds and planting, among others.

Applications having instructions configured in this way may beimplemented for different computing device platforms while retaining thesame general user interface appearance. For example, the mobileapplication may be programmed for execution on tablets, smartphones, orserver computers that are accessed using browsers at client computers.Further, the mobile application as configured for tablet computers orsmartphones may provide a full app experience or a cab app experiencethat is suitable for the display and processing capabilities of cabcomputer 115. For example, referring now to view (b) of FIG. 2 , in oneembodiment a cab computer application 220 may comprise maps-cabinstructions 222, remote view instructions 224, data collect andtransfer instructions 226, machine alerts instructions 228, scripttransfer instructions 230, and scouting-cab instructions 232. The codebase for the instructions of view (b) may be the same as for view (a)and executables implementing the code may be programmed to detect thetype of platform on which they are executing and to expose, through agraphical user interface, only those functions that are appropriate to acab platform or full platform. This approach enables the system torecognize the distinctly different user experience that is appropriatefor an in-cab environment and the different technology environment ofthe cab. The maps-cab instructions 222 may be programmed to provide mapviews of fields, farms or regions that are useful in directing machineoperation. The remote view instructions 224 may be programmed to turnon, manage, and provide views of machine activity in real-time or nearreal-time to other computing devices connected to the system 130 viawireless networks, wired connectors or adapters, and the like. The datacollect and transfer instructions 226 may be programmed to turn on,manage, and provide transfer of data collected at sensors andcontrollers to the system 130 via wireless networks, wired connectors oradapters, and the like. The machine alerts instructions 228 may beprogrammed to detect issues with operations of the machine or tools thatare associated with the cab and generate operator alerts. The scripttransfer instructions 230 may be configured to transfer in scripts ofinstructions that are configured to direct machine operations or thecollection of data. The scouting-cab instructions 232 may be programmedto display location-based alerts and information received from thesystem 130 based on the location of the agricultural apparatus 111 orsensors 112 in the field and ingest, manage, and provide transfer oflocation-based scouting observations to the system 130 based on thelocation of the agricultural apparatus 111 or sensors 112 in the field.

2.3. Data Ingest to the Computer System

In an embodiment, external data server computer 108 stores external data110, including soil data representing soil composition for the one ormore fields and weather data representing temperature and precipitationon the one or more fields. The weather data may include past and presentweather data as well as forecasts for future weather data. In anembodiment, external data server computer 108 comprises a plurality ofservers hosted by different entities. For example, a first server maycontain soil composition data while a second server may include weatherdata. Additionally, soil composition data may be stored in multipleservers. For example, one server may store data representing percentageof sand, silt, and clay in the soil while a second server may store datarepresenting percentage of organic matter (OM) in the soil.

In an embodiment, remote sensor 112 comprises one or more sensors thatare programmed or configured to produce one or more observations. Remotesensor 112 may be aerial sensors, such as satellites, vehicle sensors,planting equipment sensors, tillage sensors, fertilizer or insecticideapplication sensors, harvester sensors, and any other implement capableof receiving data from the one or more fields. In an embodiment,application controller 114 is programmed or configured to receiveinstructions from agricultural intelligence computer system 130.Application controller 114 may also be programmed or configured tocontrol an operating parameter of an agricultural vehicle or implement.For example, an application controller may be programmed or configuredto control an operating parameter of a vehicle, such as a tractor,planting equipment, tillage equipment, fertilizer or insecticideequipment, harvester equipment, or other farm implements such as a watervalve. Other embodiments may use any combination of sensors andcontrollers, of which the following are merely selected examples.

The system 130 may obtain or ingest data under user 102 control, on amass basis from a large number of growers who have contributed data to ashared database system. This form of obtaining data may be termed“manual data ingest” as one or more user-controlled computer operationsare requested or triggered to obtain data for use by the system 130. Asan example, the CLIMATE FIELDVIEW application, commercially availablefrom The Climate Corporation, San Francisco, Calif., may be operated toexport data to system 130 for storing in the repository 160.

For example, seed monitor systems can both control planter apparatuscomponents and obtain planting data, including signals from seed sensorsvia a signal harness that comprises a CAN backbone and point-to-pointconnections for registration and/or diagnostics. Seed monitor systemscan be programmed or configured to display seed spacing, population andother information to the user via the cab computer 115 or other deviceswithin the system 130. Examples are disclosed in U.S. Pat. No. 8,738,243and US Pat. Pub. 20150094916, and the present disclosure assumesknowledge of those other patent disclosures.

Likewise, yield monitor systems may contain yield sensors for harvesterapparatus that send yield measurement data to the cab computer 115 orother devices within the system 130. Yield monitor systems may utilizeone or more remote sensors 112 to obtain grain moisture measurements ina combine or other harvester and transmit these measurements to the uservia the cab computer 115 or other devices within the system 130.

In an embodiment, examples of sensors 112 that may be used with anymoving vehicle or apparatus of the type described elsewhere hereininclude kinematic sensors and position sensors. Kinematic sensors maycomprise any of speed sensors such as radar or wheel speed sensors,accelerometers, or gyros. Position sensors may comprise GPS receivers ortransceivers, or WiFi-based position or mapping apps that are programmedto determine location based upon nearby WiFi hotspots, among others.

In an embodiment, examples of sensors 112 that may be used with tractorsor other moving vehicles include engine speed sensors, fuel consumptionsensors, area counters or distance counters that interact with GPS orradar signals, PTO (power take-off) speed sensors, tractor hydraulicssensors configured to detect hydraulics parameters such as pressure orflow, and/or and hydraulic pump speed, wheel speed sensors or wheelslippage sensors. In an embodiment, examples of controllers 114 that maybe used with tractors include hydraulic directional controllers,pressure controllers, and/or flow controllers; hydraulic pump speedcontrollers; speed controllers or governors; hitch position controllers;or wheel position controllers provide automatic steering.

In an embodiment, examples of sensors 112 that may be used with seedplanting equipment such as planters, drills, or air seeders include seedsensors, which may be optical, electromagnetic, or impact sensors;downforce sensors such as load pins, load cells, pressure sensors; soilproperty sensors such as reflectivity sensors, moisture sensors,electrical conductivity sensors, optical residue sensors, or temperaturesensors; component operating criteria sensors such as planting depthsensors, downforce cylinder pressure sensors, seed disc speed sensors,seed drive motor encoders, seed conveyor system speed sensors, or vacuumlevel sensors; or pesticide application sensors such as optical or otherelectromagnetic sensors, or impact sensors. In an embodiment, examplesof controllers 114 that may be used with such seed planting equipmentinclude: toolbar fold controllers, such as controllers for valvesassociated with hydraulic cylinders; downforce controllers, such ascontrollers for valves associated with pneumatic cylinders, airbags, orhydraulic cylinders, and programmed for applying downforce to individualrow units or an entire planter frame; planting depth controllers, suchas linear actuators; metering controllers, such as electric seed meterdrive motors, hydraulic seed meter drive motors, or swath controlclutches; hybrid selection controllers, such as seed meter drive motors,or other actuators programmed for selectively allowing or preventingseed or an air-seed mixture from delivering seed to or from seed metersor central bulk hoppers; metering controllers, such as electric seedmeter drive motors, or hydraulic seed meter drive motors; seed conveyorsystem controllers, such as controllers for a belt seed deliveryconveyor motor; marker controllers, such as a controller for a pneumaticor hydraulic actuator; or pesticide application rate controllers, suchas metering drive controllers, orifice size or position controllers.

In an embodiment, examples of sensors 112 that may be used with tillageequipment include position sensors for tools such as shanks or discs;tool position sensors for such tools that are configured to detectdepth, gang angle, or lateral spacing; downforce sensors; or draft forcesensors. In an embodiment, examples of controllers 114 that may be usedwith tillage equipment include downforce controllers or tool positioncontrollers, such as controllers configured to control tool depth, gangangle, or lateral spacing.

In an embodiment, examples of sensors 112 that may be used in relationto apparatus for applying fertilizer, insecticide, fungicide and thelike, such as on-planter starter fertilizer systems, subsoil fertilizerapplicators, or fertilizer sprayers, include: fluid system criteriasensors, such as flow sensors or pressure sensors; sensors indicatingwhich spray head valves or fluid line valves are open; sensorsassociated with tanks, such as fill level sensors; sectional orsystem-wide supply line sensors, or row-specific supply line sensors; orkinematic sensors such as accelerometers disposed on sprayer booms. Inan embodiment, examples of controllers 114 that may be used with suchapparatus include pump speed controllers; valve controllers that areprogrammed to control pressure, flow, direction, PWM and the like; orposition actuators, such as for boom height, subsoiler depth, or boomposition.

In an embodiment, examples of sensors 112 that may be used withharvesters include yield monitors, such as impact plate strain gauges orposition sensors, capacitive flow sensors, load sensors, weight sensors,or torque sensors associated with elevators or augers, or optical orother electromagnetic grain height sensors; grain moisture sensors, suchas capacitive sensors; grain loss sensors, including impact, optical, orcapacitive sensors; header operating criteria sensors such as headerheight, header type, deck plate gap, feeder speed, and reel speedsensors; separator operating criteria sensors, such as concaveclearance, rotor speed, shoe clearance, or chaffer clearance sensors;auger sensors for position, operation, or speed; or engine speedsensors. In an embodiment, examples of controllers 114 that may be usedwith harvesters include header operating criteria controllers forelements such as header height, header type, deck plate gap, feederspeed, or reel speed; separator operating criteria controllers forfeatures such as concave clearance, rotor speed, shoe clearance, orchaffer clearance; or controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 that may be used with graincarts include weight sensors, or sensors for auger position, operation,or speed. In an embodiment, examples of controllers 114 that may be usedwith grain carts include controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 and controllers 114 may beinstalled in unmanned aerial vehicle (UAV) apparatus or “drones.” Suchsensors may include cameras with detectors effective for any range ofthe electromagnetic spectrum including visible light, infrared,ultraviolet, near-infrared (NIR), and the like; accelerometers;altimeters; temperature sensors; humidity sensors; pitot tube sensors orother airspeed or wind velocity sensors; battery life sensors; or radaremitters and reflected radar energy detection apparatus. Suchcontrollers may include guidance or motor control apparatus, controlsurface controllers, camera controllers, or controllers programmed toturn on, operate, obtain data from, manage and configure any of theforegoing sensors. Examples are disclosed in U.S. patent applicationSer. No. 14/831,165 and the present disclosure assumes knowledge of thatother patent disclosure.

In an embodiment, sensors 112 and controllers 114 may be affixed to soilsampling and measurement apparatus that is configured or programmed tosample soil and perform soil chemistry tests, soil moisture tests, andother tests pertaining to soil. For example, the apparatus disclosed inU.S. Pat. Nos. 8,767,194 and 8,712,148 may be used, and the presentdisclosure assumes knowledge of those patent disclosures.

In an embodiment, sensors 112 and controllers 114 may comprise weatherdevices for monitoring weather conditions of fields. For example, theapparatus disclosed in U.S. Provisional Application No. 62/154,207,filed on Apr. 29, 2015, U.S. Provisional Application No. 62/175,160,filed on Jun. 12, 2015, U.S. Provisional Application No. 62/198,060,filed on Jul. 28, 2015, and U.S. Provisional Application No. 62/220,852,filed on Sep. 18, 2015, may be used, and the present disclosure assumesknowledge of those patent disclosures.

2.4. Process Overview-Agronomic Model Training

In an embodiment, the agricultural intelligence computer system 130 isprogrammed or configured to create an agronomic model. In this context,an agronomic model is a data structure in memory of the agriculturalintelligence computer system 130 that comprises field data 106, such asidentification data and harvest data for one or more fields. Theagronomic model may also comprise calculated agronomic properties whichdescribe either conditions which may affect the growth of one or morecrops on a field, or properties of the one or more crops, or both.Additionally, an agronomic model may comprise recommendations based onagronomic factors such as crop recommendations, irrigationrecommendations, planting recommendations, and harvestingrecommendations. The agronomic factors may also be used to estimate oneor more crop related results, such as agronomic yield. The agronomicyield of a crop is an estimate of quantity of the crop that is produced,or in some examples the revenue or profit obtained from the producedcrop.

In an embodiment, the agricultural intelligence computer system 130 mayuse a preconfigured agronomic model to calculate agronomic propertiesrelated to currently received location and crop information for one ormore fields. The preconfigured agronomic model is based upon previouslyprocessed field data, including but not limited to, identification data,harvest data, fertilizer data, and weather data. The preconfiguredagronomic model may have been cross validated to ensure accuracy of themodel. Cross validation may include comparison to ground truthing thatcompares predicted results with actual results on a field, such as acomparison of precipitation estimate with a rain gauge or sensorproviding weather data at the same or nearby location or an estimate ofnitrogen content with a soil sample measurement.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using field data provided by one or more data sources.FIG. 3 may serve as an algorithm or instructions for programming thefunctional elements of the agricultural intelligence computer system 130to perform the operations that are now described.

At block 305, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic data preprocessing offield data received from one or more data sources. The field datareceived from one or more data sources may be preprocessed for thepurpose of removing noise and distorting effects within the agronomicdata including measured outliers that would bias received field datavalues. Embodiments of agronomic data preprocessing may include, but arenot limited to, removing data values commonly associated with outlierdata values, specific measured data points that are known tounnecessarily skew other data values, data smoothing techniques used toremove or reduce additive or multiplicative effects from noise, andother filtering or data derivation techniques used to provide cleardistinctions between positive and negative data inputs.

At block 310, the agricultural intelligence computer system 130 isconfigured or programmed to perform data subset selection using thepreprocessed field data in order to identify datasets useful for initialagronomic model generation. The agricultural intelligence computersystem 130 may implement data subset selection techniques including, butnot limited to, a genetic algorithm method, an all subset models method,a sequential search method, a stepwise regression method, a particleswarm optimization method, and an ant colony optimization method. Forexample, a genetic algorithm selection technique uses an adaptiveheuristic search algorithm, based on evolutionary principles of naturalselection and genetics, to determine and evaluate datasets within thepreprocessed agronomic data.

At block 315, the agricultural intelligence computer system 130 isconfigured or programmed to implement field dataset evaluation. In anembodiment, a specific field dataset is evaluated by creating anagronomic model and using specific quality thresholds for the createdagronomic model. Agronomic models may be compared using cross validationtechniques including, but not limited to, root mean square error ofleave-one-out cross validation (RMSECV), mean absolute error, and meanpercentage error. For example, RMSECV can cross validate agronomicmodels by comparing predicted agronomic property values created by theagronomic model against historical agronomic property values collectedand analyzed. In an embodiment, the agronomic dataset evaluation logicis used as a feedback loop where agronomic datasets that do not meetconfigured quality thresholds are used during future data subsetselection steps (block 310).

At block 320, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic model creation basedupon the cross validated agronomic datasets. In an embodiment, agronomicmodel creation may implement multivariate regression techniques tocreate preconfigured agronomic data models.

At block 325, the agricultural intelligence computer system 130 isconfigured or programmed to store the preconfigured agronomic datamodels for future field data evaluation.

2.5. Implementation Example—Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 4 is a block diagram that illustrates a computersystem 400 upon which an embodiment of the invention may be implemented.Computer system 400 includes a bus 402 or other communication mechanismfor communicating information, and a hardware processor 404 coupled withbus 402 for processing information. Hardware processor 404 may be, forexample, a general purpose microprocessor.

Computer system 400 also includes a main memory 406, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 402for storing information and instructions to be executed by processor404. Main memory 406 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 404. Such instructions, when stored innon-transitory storage media accessible to processor 404, rendercomputer system 400 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 400 further includes a read only memory (ROM) 408 orother static storage device coupled to bus 402 for storing staticinformation and instructions for processor 404. A storage device 410,such as a magnetic disk, optical disk, or solid-state drive is providedand coupled to bus 402 for storing information and instructions.

Computer system 400 may be coupled via bus 402 to a display 412, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 414, including alphanumeric and other keys, is coupledto bus 402 for communicating information and command selections toprocessor 404. Another type of user input device is cursor control 416,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 404 and forcontrolling cursor movement on display 412. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer system 400 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 400 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 400 in response to processor 404 executing one or more sequencesof one or more instructions contained in main memory 406. Suchinstructions may be read into main memory 406 from another storagemedium, such as storage device 410. Execution of the sequences ofinstructions contained in main memory 406 causes processor 404 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical disks, magnetic disks, or solid-state drives, suchas storage device 410. Volatile media includes dynamic memory, such asmain memory 406. Common forms of storage media include, for example, afloppy disk, a flexible disk, hard disk, solid-state drive, magnetictape, or any other magnetic data storage medium, a CD-ROM, any otheroptical data storage medium, any physical medium with patterns of holes,a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 402. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 404 for execution. For example,the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 400 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 402. Bus 402 carries the data tomain memory 406, from which processor 404 retrieves and executes theinstructions. The instructions received by main memory 406 mayoptionally be stored on storage device 410 either before or afterexecution by processor 404.

Computer system 400 also includes a communication interface 418 coupledto bus 402. Communication interface 418 provides a two-way datacommunication coupling to a network link 420 that is connected to alocal network 422. For example, communication interface 418 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 418 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 418sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 420 typically provides data communication through one ormore networks to other data devices. For example, network link 420 mayprovide a connection through local network 422 to a host computer 424 orto data equipment operated by an Internet Service Provider (ISP) 426.ISP 426 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 428. Local network 422 and Internet 428 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 420and through communication interface 418, which carry the digital data toand from computer system 400, are example forms of transmission media.

Computer system 400 can send messages and receive data, includingprogram code, through the network(s), network link 420 and communicationinterface 418. In the Internet example, a server 430 might transmit arequested code for an application program through Internet 428, ISP 426,local network 422 and communication interface 418.

The received code may be executed by processor 404 as it is received,and/or stored in storage device 410, or other non-volatile storage forlater execution.

3. DETERMINING RISK OF DISEASE

3.1. Received Data

FIG. 7 depicts a method for determining a risk of disease of a crop on afield based on received data regarding the crop.

At step 702 environmental risk data, crop data, and crop management datarelating to one or more crops on a field are received. For example,agricultural intelligence computer system 130 may receive data from thefield manager computing devices 104 regarding the one or more fields.Additionally or alternatively, agricultural intelligence computer system130 may receive information regarding the one or more fields associatedwith the field manager computing devices 104 from one or more remotesensors on or about the one or more fields, one or more satellites, oneor more manned or unmanned aerial vehicles (MAVs or UAVs), one or moreon-the-go sensors, and/or one or more external data servers 108. Thedata may include field descriptions, soil data, planting data, fertilitydata, harvest and yield data, crop protection data, pest and diseasedata, irrigation data, tiling data, imagery, weather data, andadditional management data.

Environmental risk data may identify a risk of disease based on changesin the environment. Changes in the environment may include changes intemperature and humidity. The environmental risk data may indicate anumber of risk hours and/or risk days that have accumulated betweenplanting of a crop and a time or date of risk assessment. Risk hours andrisk days, as used herein, refer to hours and days respectively wherethe crop is considered to be at risk of developing the disease based onenvironmental data. For instance, a risk hour may be identified if thetemperature at the field is within a first range of values and thehumidity at the field is within a second range of values. In anembodiment, environmental risk data includes time series data. Theenvironmental risk data may indicate which hours between planting and atime of risk assessment were identified as risk hours. As an example,the environmental risk data may indicate that five hours on the dayafter planting were identified as risk hours, but only four hours in thenext day were identified as risk hours.

Crop data may include data about the crop itself. For example, crop datamay include identification of the type of hybrid seed that has beenplanted, one or more values indicating a tolerance of the seed to one ormore types of diseases, and/or relative maturity rating of the seed.Crop management data may include data regarding management of the field.For example, crop management data may include identification of a numberof days and/or growing degree days between planting of the crop and atime of risk assessment, prior planting and harvesting data, presence orabsence of tillage on the field, and/or a type of tillage used on thefield.

3.2. Factor Generation

At step 704, the process computes one or more crop risk factors based,at least in part, on the crop data. For example, agriculturalintelligence computer system 130 may translate the received crop datainto one or more factors which can then be used to calculate a risk ofdisease on the field. The crop risk factors may include factors based onthe hybrid seed type and/or the relative maturity of the hybrid seedtype.

Translating the identification of the type of hybrid seed planted to acrop risk factor may include accessing data which associates differenthybrid seed types with a susceptibility value. For example, differenthybrid seeds may be ranked based on susceptibility to different types ofdisease. The rankings may be based on previous field trials and/orpublished data of the seeds. The rankings may be normalized to valuesbetween −1 and 1 where a value of 1 indicates that the seed is the mostsusceptible to disease while a −1 indicates the seed is leastsusceptible to disease. In an embodiment, where the hybrid seed type isunknown, a seed type factor may be set to an average value. For example,where seed types are normalized to values between −1 and 1, an unknownseed type may be assigned a value of 0.

A relative maturity factor may include an integer of the relativematurity of the seed. Where data identifying a seed type is received,agricultural intelligence computer system 130 may determine a relativematurity based on the seed type. For example, agricultural intelligencecomputer system 130 may access data stored on agricultural intelligencecomputer system 130 or an external server computer which identifiesrelative maturity of different types of seeds. Agricultural intelligencecomputer system 130 may retrieve the relative maturity value for theseed type and use the relative maturity value as a relative maturityfactor. Additionally or alternatively, the relative maturity factor maybe a normalized version of the relative maturity integer value.

At step 706, the process computes one or more environmental risk factorsbased, at least in part, on the environmental risk data. For example,agricultural intelligence computer system 130 may compute one or more ofa cumulative disease risk, an integral of the cumulative disease risk, anormalized cumulative disease risk, and/or a normalized integral of thecumulative disease risk.

The cumulative disease risk may be computed as a summation of risk hoursand/or risk days up until a measurement day. For example, the cumulativedisease risk for a day that is x days after planting of the crop may becomputed as:

${C(x)} = {\sum\limits_{{day} = 1}^{x}{{risk}({day})}}$where C is the cumulative disease risk x days after planting of the cropand risk(day) is an environmental risk value for the day. The risk(day)value may be an accumulation of risk hours for the day and/or a valueindicating whether the day was considered a risk day or not.

The integral of the cumulative disease risk may be computed as anaccumulation of the cumulative disease risk for each day up until ameasurement day. For example, the integral of the cumulative diseaserisk for a day that is x days after planting of the crop may be computedas:

${I(x)} = {\sum\limits_{{day} = 1}^{x}{C({day})}}$where I is the integral of the cumulative disease risk x days afterplanting of the crop and C(day) is the cumulative risk disease for theday. The integral of the cumulative disease risk values risk hoursand/or risk days that occurred closer to planting over risk hours and/orrisk days that occurred further from planting.

The normalized cumulative risk may be computed as the average dailydisease risk whereas the normalized integral may be computed as afunction of integral of cumulative disease risk and time. For instance,the normalized integral may be computed as the integral of cumulativedisease risk divided by the integral of cumulative number of days afterplanting. As an example, the normalized cumulative disease risk and thenormalized integral of the cumulative disease risk may be computed asfollows:

${C_{n}(x)} = \frac{C(x)}{x}$${I_{n}(x)} = \frac{I(x)}{{x\left( {1 + x} \right)}/2}$where C_(n)(x) is the normalized cumulative disease risk x days afterplanting of the crop and I_(n)(x) is the normalized integral ofcumulative disease risk x days after planting of the crop.

At step 708, the process computes one or more crop management riskfactors based, at least in part, on the crop management data. Forexample, agricultural intelligence computer system 130 may translate thecrop management data into crop management factors using data stored inagricultural intelligence computer system 130. The identification of anumber of days and/or growing degree days between planting of the cropand a time of risk assessment may be used in the environmental riskfactor calculations described above and/or used as their own factor.Other management data, such as prior planting and harvesting data,presence or absence of tillage on the field, and/or a type of tillageused on the field may be translated into values indicating increase inrisk or decrease in risk.

Agricultural intelligence computer system 130 may store data indicatingincrease or decrease in risk due to different management practices. Forexample, crop rotation may be identified as decreasing the risk ofdisease while an absence of crop rotation may be identified asincreasing the risk of disease. Thus, crop rotation may be assigned avalue of −1 while an absence of crop rotation is assigned a value of 1.Agricultural intelligence computer system 130 may assign a value of 0when there is not enough prior planting data to determine if there hasbeen crop rotation. Additionally or alternatively, agriculturalintelligence computer system 130 may assign values for crop rotationbetween −1 and 1 based on a portion of the field that has received croprotation. For example, if half of the field rotated crops, agriculturalintelligence computer system 130 may assign a value of 0 where if threequarters of the field rotated crops, agricultural intelligence computersystem 130 may assign a value of 0.5.

Presence or absence of tillage on the field may be treated similarly aspresence and absence of crop rotation. For example, the presence oftillage may be assigned a value of −1 while the absence of tillage isassigned a value of 1. Agricultural intelligence computer system 130 mayassign a value of 0 if there is no tillage information for the field.Additionally or alternatively, agricultural intelligence computer system130 may assign values for the presence or absence of tillage between −1and 1 based on a portion of the field that has received tillage.

For harvesting data and type of tillage, agricultural intelligencecomputer system 130 may assign values to different types of harvestingand different types of tillage based on the amount that the harvestingtype and/or tillage type affects the risk of disease. For example,conventional tillage which tends to bury a large amount of residue maybe assigned a value closer to −1 while minimal tillage may be assigned avalue closer to 1. Other types of tillage may be assigned a range ofnumbers based on how well they bury residue or decrease risk of diseasein the field. Harvest types may be treated similarly, where harvesttypes that remove a larger amount of residue are assigned values closerto −1 while harvest types that leave behind a larger amount of residueare assigned values closer to 1.

Irrigation and fungicide factors additionally may be generated byagricultural intelligence computer system 130 based on irrigation andfungicide data. For example, agricultural intelligence computer system130 may receive data identifying a date/time of fungicide application,an amount of fungicide applied, and an area of the field to which thefungicide was applied. For a given day, the fungicide factor may bebased on a number of days since the last fungicide application an amountof fungicide applied, and/or an area of the field to which the fungicidewas applied. For example, a fungicide factor may be computed as:f=1−2(A−t)where f is the fungicide factor, A is the percentage of the field towhich fungicide was applied, and t is a time value which equals 0 on thedate of fungicide application and approaches one as the number of dayssince application approaches a particular value. For example, if afungicide is assumed to be no longer effective after thirty days, then tmay approach 1 as the number of days since the application approachesthirty. A second factor for a type of fungicide may be used whichapproaches −1 for stronger fungicides and approaches 0 for weakerfungicides.

A similar equation may be used for irrigation which increases themoisture and thereby additionally increases a likelihood of higherhumidity. The irrigation factor may additionally comprise a value whichapproaches one the closer to a time of irrigation and approachesnegative one the further the crop is from irrigation. In embodimentswhere agricultural intelligence computer system 130 receives soilmoisture data, agricultural intelligence computer system 130 mayassociate higher soil moistures with values closer to 1 and lower soilmoistures with values closer to −1. As a result of completing steps 702to 708, the server computer is able to model a probability of diseaseonset using a plurality of different factors based on received data.

3.3. Digital Disease Modeling

At step 710, the process uses a digital model of disease probability tocompute a probability of onset of a particular disease for the one ormore crops on the field based, at least in part, on the one or more croprisk factors, the one or more environmental risk factors, and the one ormore crop management risk factors. The crop risk factors may include oneor more of a seed type factor or a relative maturity of the seed. Theenvironmental risk factors may include one or more of the cumulativeenvironmental risk, the integral of the cumulative environmental risk,the normalized cumulative environmental risk, or the normalized integralof the cumulative environmental risk, and/or other computations ofenvironmental risk based on environmental conditions favorable todisease. The crop management risk factors may include one or more of thecrop rotation factors, the presence or absence of tillage factor, theharvesting data factor, the tillage type factor, the fungicide factor,the irrigation factor, or the soil moisture factor.

In an embodiment, agricultural intelligence computer system 130 trains amodel of disease probability using training data comprising one or morerisk factors as inputs and a presence or absence of disease as outputs.For example, agricultural intelligence computer system 130 may train amodel based on reports of diseases identified on a field, such asnorthern leaf blight and gray leaf spot. Agricultural intelligencecomputer system 130 may receive a plurality of training datasets, eachof which identifying a state of one or more factors as well as whetherthe crop was observed with the disease or without the disease. Forexample, a first training dataset may indicate the following:

-   -   presence of disease=y    -   days after planting=87    -   crop rotation=no    -   tillage=yes    -   tillage type=minimal till    -   risk hours per day={3, 5, . . . , 0}    -   fungicide application=no    -   irrigation=no    -   relative maturity=93    -   hybrid risk level=5        where the hybrid risk level is an estimated risk for a        particular type of hybrid seed. The hybrid risk level may be        received from one or more external servers based on the hybrid        seed type and/or determined at agricultural intelligence        computer system 130 based on the hybrid seed type. Agricultural        intelligence computer system 130 may convert the data in the        training dataset to factors as described above and use the        factors to train a digital model of disease probability.

In an embodiment, agricultural intelligence computer system 130 trainsmodels of disease probability for different geographic locations. Forexample, agricultural intelligence computer system 130 may receive, withthe training datasets, data identifying a location of the field, such aslatitude and longitude. Agricultural intelligence computer system 130may select a range of latitudes and/or longitudes and train a model ofdisease probability with only training datasets associated withlocations within the range of latitudes and/or longitudes. The trainedmodel of disease probability based on datasets within the range oflatitudes and/or longitudes may be used to compute a probability ofdisease risk for one or more locations with a latitude and longitudewithin the range of latitudes and/or longitudes. Additionally oralternatively, latitude and/or longitude may be used as an input factorin the model of disease probability.

In an embodiment, the model of disease probability uses a plurality ofrandomly generated decision trees to determine a likelihood of onset ofa particular disease. For example, the model of disease probability maycomprise a random forest classifier which accepts inputs of the one ormore factors described herein and outputs a likelihood of onset of adisease for a crop on a given day. Code for implementing a random forestclassifier is readily available on public open source program coderepository systems, such as GITHUB. The random forest classifier may beused to model the probability of the presence of disease for a pluralityof days for a particular field.

In an embodiment, the model of disease probability computes theprobability of disease onset over time. For example, a survivalregression model, such as the Cox Proportional Hazard model, may betrained using one or more of the above described factors as covariates.As a survival regression model computes the probability of disease onsetover time, environmental risk hours and/or risk days may be used as aduration variable for the model. Additionally or alternatively,agricultural intelligence computer system 130 may use growing degreedays as the duration variable. When the model is run for a particularfield, data may be aggregated to identify a particular time of onset.For example, if the output of a Cox Proportional Hazard model identifiesa high risk of disease after a given day, agricultural intelligencecomputer system 130 may select the given day as the likely onset of thedisease.

3.4. Data Usage

The techniques described thus far may be implemented by computer toprovide improvements in another technology, for example plant pathology,plant pest control, agriculture, or agricultural management. Forexample, at step 712, the process may model the onset of a disease on acrop. At step 714, the process may model the probability of futuredisease onset. At step 716, the process may send applicationrecommendations to a field manager computing device. At step 718, theprocess may cause application of a product, such as a fungicide, on afield. The agricultural computer system may perform one or more of steps712-718. Each of the processes described in steps 712-718 are describedfurther herein.

In an embodiment, agricultural intelligence computer system 130 uses theprobabilities of disease to determine if a particular disease iscurrently affecting a field or has affected a field. For example,agricultural intelligence computer system 130 may receive crop data,management data, and environmental risk data for a particular field fromone or more sources such as a field manager computing device or anexternal server computer. Agricultural intelligence computer system 130may use the disease probability to model the likelihood of diseaseoccurring each day since planting. For example, agriculturalintelligence computer system 130 may use the random forest model usingdifferent datasets depending on the day or the Cox Proportional Hazardmodel to determine whether disease is likely to have appeared and whenthe disease appeared.

In an embodiment, the probabilities of disease are used to update modelsof crop yield and/or reduce a prior estimate of crop yield. For example,agricultural intelligence computer system 130 may use prior computationsof crop yield and prior identifications of disease to determine aneffect on crop yield of a particular disease. Based on the determinationthat the particular disease is currently affecting the field or hasaffected the field, agricultural intelligence computer system 130 mayadjust the crop yield for the crop using the determined effect on cropyield of the particular disease. The reduced yield value may be sent toa field manager computing device for display to a field manager or maybe used to recommend fungicide use and/or fungicide trials for futureyears.

In an embodiment, agricultural intelligence computer system 130 uses themodel of disease probability to determine a future likelihood of thepresence of a disease on the crop. For example, agriculturalintelligence computer system 130 may use fourteen-day weather forecaststo determine likely risk hours or risk days into the future. Using thelikely risk hours or risk days into the future, agriculturalintelligence computer system 130 may compute estimated environmentalrisk factors for the future. Agricultural intelligence computer system130 may then use the one or more crop risk factors, the one or more cropmanagement risk factors, and the estimated environmental risk factors tocompute likelihood of disease onset in the next fourteen days.

Agricultural intelligence computer system 130 may use the computedlikelihood of presence of a disease to generate fungiciderecommendations. For example, agricultural intelligence computer system130 may determine a likelihood of onset of the disease on the crop inthe next fourteen days using the methods described herein. Agriculturalintelligence computer system 130 may additionally determine a benefit ofapplying the fungicide. The benefit may comprise reducing the likelihoodof disease onset and/or increasing the likely yield for the crop. Ifagricultural intelligence computer system 130 determines that thedisease is likely to present on the crop within the next fourteen days,agricultural intelligence computer system 130 may generate arecommendation to apply fungicide to the crop, thereby reducing theprobability of disease. By modeling the likelihood of disease occurringin the future, agricultural intelligence computer system 130 is able togenerate recommendations that, if implemented, prevent the occurrence orspread of the disease.

In an embodiment, the fungicide recommendations are sent to a fieldmanager computing device. For example, agricultural intelligencecomputer system 130 may cause a notification to be displayed on thefield manager computing device identifying one or more fields and/or oneor more portions of the field that are likely to present with aparticular disease, thereby giving the field manager the opportunity toprevent or limit the progression of the disease. The fungiciderecommendation may identify a likely benefit to the field of applyingthe fungicide. For example, agricultural intelligence computer system130 may compute an estimate of yield loss if disease presents. Based onthe estimate of loss, agricultural intelligence computer system 130 maydetermine a benefit to crop yield and/or revenue of applying thefungicide. The fungicide recommendation may identify the likely increasein crop yield and/or revenue for applying the fungicide.

Additionally or alternatively, agricultural intelligence computer system130 may cause implementation of the fungicide recommendation on one ormore fields. For example, agricultural intelligence computer system 130may generate a script which, when executed by an application controller,causes the application controller to control a field implement whichreleases fungicide onto a field. Thus, agricultural intelligencecomputer system 130 may determine whether a disease is likely to presentwithin a particular period of time and, in response, cause prevention ofthe disease through application of a fungicide.

In an embodiment, agricultural intelligence computer system 130continuously monitors values for a particular field in order todetermine when to apply a fungicide. For example, if agriculturalintelligence computer system 130 has access to fourteen-day forecasts,agricultural intelligence computer system 130 may periodically compute alikelihood of a disease presenting within fourteen days of thecomputation. Thus, as the growing season progresses, agriculturalintelligence computer system 130 may track the likelihood of diseasepresenting on the field and generate fungicide recommendations as thelikelihood increases. For instance, agricultural intelligence computersystem 130 may do new computations every seven days using fourteen-dayforecasts. When agricultural intelligence computer system 130 detectslikely occurrence of the disease in a computation, agriculturalintelligence computer system 130 may generate a fungiciderecommendation.

In an embodiment, agricultural intelligence computer system 130 uses thecomputations of disease risk to recommend different actions for thefield manager for upcoming seasons. For example, if agriculturalintelligence computer system 130 determines that disease presented onthe field, agricultural intelligence computer system 130 may computelikelihood that disease would have presented given a different type oftillage, different type of hybrid seed planted, different type ofharvesting, crop rotation, or one or more other different managementpractices using the methods described herein. For instance, if minimaltillage was initially used, agricultural intelligence computer system130 may compute the likelihood that disease presented if conventionaltillage was used. If agricultural intelligence computer system 130determines that changing the tillage type would likely have caused thedisease to not present and/or reduced the amount of fungicide needed tokeep the disease from presenting, agricultural intelligence computersystem 130 may recommend changing the tillage type for future seasons.

4. BENEFITS OF CERTAIN EMBODIMENTS

Numerous benefits and improvements provided by the techniques hereinhave been described in the preceding section. Furthermore, using thetechniques described herein, a computing device can track the risk of adisease affecting crops on a field. Agricultural intelligence computersystem 130 may then act on that risk by either providing a field managercomputing device with a recommendation for avoiding damage to the cropbased on the risk and/or by controlling an implement on the field andcausing the implement to release fungicide onto the field. By doing so,agricultural intelligence computer system 130 provides data which can beused to protect crops, increase crop yield, and generate strongerdigital models of the crop during development.

5. EXTENSIONS AND ALTERNATIVES

In the foregoing specification, embodiments have been described withreference to numerous specific details that may vary from implementationto implementation. The specification and drawings are, accordingly, tobe regarded in an illustrative rather than a restrictive sense. The soleand exclusive indicator of the scope of the disclosure, and what isintended by the applicants to be the scope of the disclosure, is theliteral and equivalent scope of the set of claims that issue from thisapplication, in the specific form in which such claims issue, includingany subsequent correction.

What is claimed is:
 1. A method comprising: receiving environmental riskdata relating to a crop on a field; computing one or more environmentalrisk factors based on the environmental risk data, the one or moreenvironmental risk factors including at least one of a cumulativedisease risk over a period of time and an integral of a cumulativedisease risk over a period of time; computing, by a trained model ofdisease probability, a probability of onset of a particular disease forthe crop on the field based on the one or more environmental riskfactors, the trained model of disease probability having been trainedusing a training dataset comprising occurrence or non-occurrence ofdisease and the one or more environmental risk factors, wherein the oneor more environmental risk factors are used as training inputs and saidoccurrence or non-occurrence of disease is used as training outputs; andbased on the probability of onset of the particular disease, sending, toan application controller, one or more scripts, wherein the one or morescripts are executed by the application controller to cause an implementon the field to release at least one selected from the group consistingof: nutrients, insecticide, and herbicide on one or more portions of thefield based on the probability of onset of the particular disease, toprevent onset of the particular disease.
 2. The method of claim 1wherein the environmental risk data indicates a number of risk hours orrisk days in which the crop is at risk of developing the particulardisease.
 3. The method of claim 2 further comprising calculating thecumulative disease risk based on at least one of: a number of riskhours, a number of risk days, and a number of growing degree days. 4.The method of claim 1 wherein the trained model of disease probabilitycomputing the probability of onset of the particular disease comprisesdecreasing the probability of onset of the particular disease based onat least one of: increased removal of residue by harvesting, increasedburial of residue by tilling, and increased crop rotation applied to aportion of the field.
 5. The method of claim 1 wherein the trained modelof disease probability computing the probability of onset of theparticular disease is configured to increase the probability of onset ofthe particular disease based on at least one of: an increased number ofdays since a last fungicide application on the field, a decreased amountof fungicide applied to the field, and a decreased percentage of thefield to which fungicide was applied.
 6. The method of claim 1 whereinthe training dataset comprises occurrence or non-occurrence of at leastone of northern leaf blight and gray leaf spot.
 7. The method of claim 1wherein: the crop is a type of hybrid seed; and the trained model ofdisease probability computing the probability of onset of the particulardisease is based on a value indicating a tolerance of the type of hybridseed to the particular disease.
 8. The method of claim 1 wherein thetrained model of disease probability computing the probability of onsetof the particular disease is based on information about the fieldreceived from at least one of: a satellite, a manned aerial vehicle, andan unmanned aerial vehicles.
 9. The method of claim 1 further comprisingpredicting a particular time of onset of the particular disease usingthe trained model of disease probability.
 10. The method of claim 1wherein the trained model of disease probability computing theprobability of onset of the particular disease for the crop on the fieldcomprises a Cox proportional hazard model computing the probability ofonset of the particular disease for the crop on the field.
 11. One ormore computer-readable non-transitory media storing instructions that,when executed by one or more processors, cause: receiving environmentalrisk data relating to a crop on a field; computing one or moreenvironmental risk factors based on the environmental risk data, the oneor more environmental risk factors including at least one of acumulative disease risk over a period of time and an integral of acumulative disease risk over a period of time; computing, by a trainedmodel of disease probability, a probability of onset of a particulardisease for the crop on the field based on the one or more environmentalrisk factors, the trained model of disease probability having beentrained using a training dataset comprising occurrence or non-occurrenceof disease and the one or more environmental risk factors, wherein theone or more environmental risk factors are used as training inputs andsaid occurrence or non-occurrence of disease is used as trainingoutputs; and based on the probability of onset of the particulardisease, sending, to an application controller, one or more scripts,wherein the one or more scripts are executed by the applicationcontroller to cause an implement on the field to release at least oneselected from the group consisting of: nutrients, insecticide, andherbicide on one or more portions of the field based on the probabilityof onset of the particular disease, to prevent onset of the particulardisease.
 12. The one or more computer-readable non-transitory media ofclaim 11 wherein the environmental risk data indicates a number of riskhours or risk days in which the crop is at risk of developing theparticular disease.
 13. The one or more computer-readable non-transitorymedia of claim 12 wherein the instructions further cause calculating thecumulative disease risk based on at least one of: a number of riskhours, a number of risk days, and a number of growing degree days. 14.The one or more computer-readable non-transitory media of claim 11wherein the trained model of disease probability computing theprobability of onset of the particular disease comprises decreasing theprobability of onset of the particular disease based on at least one of:increased removal of residue by harvesting, increased burial of residueby tilling, and increased crop rotation applied to a portion of thefield.
 15. The one or more computer-readable non-transitory media ofclaim 11 wherein the trained model of disease probability computing theprobability of onset of the particular disease is configured to increasethe probability of onset of the particular disease based on at least oneof: an increased number of days since a last fungicide application onthe field, a decreased amount of fungicide applied to the field, and adecreased percentage of the field to which fungicide was applied. 16.The one or more computer-readable non-transitory media of claim 11wherein the training dataset comprises occurrence or non-occurrence ofat least one of northern leaf blight and gray leaf spot.
 17. The one ormore computer-readable non-transitory media of claim 11 wherein: thecrop is a type of hybrid seed; and the trained model of diseaseprobability computing the probability of onset of the particular diseaseis based on a value indicating a tolerance of the type of hybrid seed tothe particular disease.
 18. The one or more computer-readablenon-transitory media of claim 11 wherein the trained model of diseaseprobability computing the probability of onset of the particular diseaseis based on information about the field received from at least one of: asatellite, a manned aerial vehicle, and an unmanned aerial vehicles. 19.The one or more computer-readable non-transitory media of claim 11wherein the instructions further cause predicting a particular time ofonset of the particular disease using the trained model of diseaseprobability.
 20. The one or more computer-readable non-transitory mediaof claim 11 wherein the trained model of disease probability computingthe probability of onset of the particular disease for the crop on thefield comprises a Cox proportional hazard model computing theprobability of onset of the particular disease for the crop on thefield.